Why AI Coding Tools Should Be Measured by Retained Changes, Not Accepted Suggestions

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Tracking AI coding assistant performance by accepted suggestions is misleading, as code can be rewritten or reverted after acceptance while still counting as a success. A more reliable approach connects three data points — acceptance, merge status, and retention over time — to form a funnel metric rather than a single flattering percentage. The recommended unit is cost per retained task, calculated by dividing total tool, review, and rework costs by the number of changes that remain stable after a defined period, such as 14 days. Teams should segment results by task type and repository, and also track counter-metrics like rollback rate, escaped defects, and review time to avoid skewed averages. Expansion of AI tool usage should only proceed if retained-task cost improves on existing workflows without increasing rollback rates, with a baseline recorded before the tool is enabled.

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